Classification of Volumetric Images Using Multi-Instance Learning and Extreme Value Theorem
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Classification of Volumetric Images Using Multi-Instance Learning and Extreme Value Theorem. / Tennakoon, Ruwan; Bortsova, Gerda; Orting, Silas; Gostar, Amirali K; Wille, Mathilde M W; Saghir, Zaigham; Hoseinnezhad, Reza; de Bruijne, Marleen; Bab-Hadiashar, Alireza.
In: IEEE Transactions on Medical Imaging, Vol. 39, No. 4, 2020, p. 854-865.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Classification of Volumetric Images Using Multi-Instance Learning and Extreme Value Theorem
AU - Tennakoon, Ruwan
AU - Bortsova, Gerda
AU - Orting, Silas
AU - Gostar, Amirali K
AU - Wille, Mathilde M W
AU - Saghir, Zaigham
AU - Hoseinnezhad, Reza
AU - de Bruijne, Marleen
AU - Bab-Hadiashar, Alireza
PY - 2020
Y1 - 2020
N2 - Volumetric imaging is an essential diagnostic tool for medical practitioners. The use of popular techniques such as convolutional neural networks (CNN) for analysis of volumetric images is constrained by the availability of detailed (with local annotations) training data and GPU memory. In this paper, the volumetric image classification problem is posed as a multiinstance classification problem and a novel method is proposed to adaptively select positive instances from positive bags during the training phase. This method uses the extreme value theory to model the feature distribution of the images without a pathology and use it to identify positive instances of an imaged pathology. The experimental results, on three separate image classification tasks (i.e. classify retinal OCT images according to the presence or absence of fluid build-ups, emphysema detection in pulmonary 3D-CT images and detection of cancerous regions in 2D histopathology images) show that the proposed method produces classifiers that have similar performance to fully supervised methods and achieves the state of the art performance in all examined test cases.
AB - Volumetric imaging is an essential diagnostic tool for medical practitioners. The use of popular techniques such as convolutional neural networks (CNN) for analysis of volumetric images is constrained by the availability of detailed (with local annotations) training data and GPU memory. In this paper, the volumetric image classification problem is posed as a multiinstance classification problem and a novel method is proposed to adaptively select positive instances from positive bags during the training phase. This method uses the extreme value theory to model the feature distribution of the images without a pathology and use it to identify positive instances of an imaged pathology. The experimental results, on three separate image classification tasks (i.e. classify retinal OCT images according to the presence or absence of fluid build-ups, emphysema detection in pulmonary 3D-CT images and detection of cancerous regions in 2D histopathology images) show that the proposed method produces classifiers that have similar performance to fully supervised methods and achieves the state of the art performance in all examined test cases.
U2 - 10.1109/TMI.2019.2936244
DO - 10.1109/TMI.2019.2936244
M3 - Journal article
C2 - 31425069
VL - 39
SP - 854
EP - 865
JO - I E E E Transactions on Medical Imaging
JF - I E E E Transactions on Medical Imaging
SN - 0278-0062
IS - 4
ER -
ID: 227842826